Sleep　is　the　significant　physiological　process　to　keep　healthy.　Sleep　stage　classification　based　on　polysom⁃　nography　(PSG)　is　the　fundamental　evidence　to　diagnose　sleep　disorders　and　assess　sleep　quality.　Manual　sleep　staging　method　has　some　typical　problems　when　handling　the　large-scale　PSG　data,　such　as　time-consuming　and　low-efficiency.　The　automatic　sleep　staging　method　that　utilizing　deep　learning　models　to　effectively　learn　PSG　representations　shows　ex⁃　tensive　researching　prospects.　Most　existing　models　cannot　fully　consider　the　epoch-level　waveform　information,　channel-wise　correlations,　sequence-level　sleep　transitions.　This　paper　proposes　a　transformer-based　hierarchical　sleep　staging　model　(HierFormer),　which　employs　transformer　encoder　to　extract　valid　epoch-level　waveform　features,　channel-wise　correlation　features,　sequence-level　transition　features.　Meanwhile,　it　adopts　attention　mechanism　to　improve　the　model　interpretability　of　signal　properties　from　three　views,　including　epoch-level,　channel-wise,　and　sequence-level　views.　Experimental　results　on　the　sleep-european　data　format　(sleep-EDF)　database　expanded　dataset　show　that　the　proposed　model　achieves　better　sleep　staging　performance　with　less　parameters　compared　with　various　baseline　models.　The　overall　accuracy,　macro-aver⁃　aging　precision,　macro-averaging　recall,　macro-averaging　F1-score,　and　Cohen’s-kappa　coefficient　achieve　0.807,　0.784,　0.735,　0.750,　and　0.721,　respectively.　According　to　the　performance　comparisons　of　different　feature　encoding　methods　from　three　views　and　the　visualization　of　attention　weights,　this　paper　further　demonstrates　the　satisfied　encoding　ability　and　interpretability　of　proposed　model.　This　study　aims　to　provide　innovative　deep　learning　approaches　and　technologies　for　the　research　of　sleep　staging　applications,　thus　assisting　sleep　experts　to　improve　the　efficiency　of　sleep　disorder　diagnosis　and　treatment.　©　2025　Chinese　Institute　of　Electronics.　All　rights　reserved.